


import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
A			:						Any        =  logging.getLogger(__name__)


A			:						int        =  'Hello world! cécé herlolip'


A			:						Optional[int]        =  namedtuple(
    'BertAbsConfig',
    [
        'temp_dir',
        'large',
        'use_bert_emb',
        'finetune_bert',
        'encoder',
        'share_emb',
        'max_pos',
        'enc_layers',
        'enc_hidden_size',
        'enc_heads',
        'enc_ff_size',
        'enc_dropout',
        'dec_layers',
        'dec_hidden_size',
        'dec_heads',
        'dec_ff_size',
        'dec_dropout',
    ],
)






def 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				List[str]	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						BertAbsConfig(
		    temp_dir="""."""       ,       finetune_bert=__magic_name__       ,       large=__magic_name__       ,       share_emb=__magic_name__       ,       use_bert_emb=__magic_name__       ,       encoder="""bert"""       ,       max_pos=512       ,       enc_layers=6       ,       enc_hidden_size=512       ,       enc_heads=8       ,       enc_ff_size=512       ,       enc_dropout=0.2       ,       dec_layers=6       ,       dec_hidden_size=768       ,       dec_heads=8       ,       dec_ff_size=2048       ,       dec_dropout=0.2       ,       )
		lowercase__	     =						torch.load(__magic_name__       ,       lambda __magic_name__       ,       __magic_name__	: storage	)
		lowercase__	     =						AbsSummarizer(__magic_name__       ,       torch.device("""cpu"""	)       ,       __magic_name__	)
		original.eval()

		lowercase__	     =						BertAbsSummarizer(__magic_name__       ,       torch.device("""cpu"""	)	)
		new_model.eval()

		# -------------------
		# Convert the weights
		# -------------------

		logging.info("""convert the model"""	)
		new_model.bert.load_state_dict(original.bert.state_dict()	)
		new_model.decoder.load_state_dict(original.decoder.state_dict()	)
		new_model.generator.load_state_dict(original.generator.state_dict()	)

		# ----------------------------------
		# Make sure the outpus are identical
		# ----------------------------------

		logging.info("""Make sure that the models' outputs are identical"""	)
		lowercase__	     =						BertTokenizer.from_pretrained("""bert-base-uncased"""	)

		# prepare the model inputs
		lowercase__	     =						tokenizer.encode("""This is sample éàalj'-."""	)
		encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__magic_name__	))	)
		lowercase__	     =						torch.tensor(__magic_name__	).unsqueeze(0	)
		lowercase__	     =						tokenizer.encode("""This is sample 3 éàalj'-."""	)
		decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__magic_name__	))	)
		lowercase__	     =						torch.tensor(__magic_name__	).unsqueeze(0	)

		# failsafe to make sure the weights reset does not affect the
		# loaded weights.
		assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight	)	) == 0

		# forward pass
		lowercase__	     =						encoder_input_ids
		lowercase__	     =						decoder_input_ids
		lowercase__	     =						lowercase__	     =						None
		lowercase__	     =						None
		lowercase__	     =						lowercase__	     =						None
		lowercase__	     =						lowercase__	     =						None
		lowercase__	     =						None

		# The original model does not apply the geneator layer immediatly but rather in
		# the beam search (where it combines softmax + linear layer). Since we already
		# apply the softmax in our generation process we only apply the linear layer here.
		# We make sure that the outputs of the full stack are identical
		lowercase__	     =						original(__magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__	)[0]
		lowercase__	     =						original.generator(__magic_name__	)

		lowercase__	     =						new_model(
		    __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__	)[0]
		lowercase__	     =						new_model.generator(__magic_name__	)

		lowercase__	     =						torch.max(torch.abs(output_converted_model - output_original_model	)	).item()
		print("""Maximum absolute difference beween weights: {:.2f}""".format(__magic_name__	)	)
		lowercase__	     =						torch.max(torch.abs(output_converted_generator - output_original_generator	)	).item()
		print("""Maximum absolute difference beween weights: {:.2f}""".format(__magic_name__	)	)

		lowercase__	     =						torch.allclose(__magic_name__       ,       __magic_name__       ,       atol=1E-3	)
		if are_identical:
				logging.info("""all weights are equal up to 1e-3"""	)
		else:
				raise ValueError("""the weights are different. The new model is likely different from the original one."""	)

		# The model has been saved with torch.save(model) and this is bound to the exact
		# directory structure. We save the state_dict instead.
		logging.info("""saving the model's state dictionary"""	)
		torch.save(
		    new_model.state_dict()       ,       """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"""	)


if __name__ == "__main__":
	A			:						List[str]        =  argparse.ArgumentParser()
	parser.add_argument(
	    '--bertabs_checkpoint_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path the official PyTorch dump.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to the output PyTorch model.',
	)
	A			:						Tuple        =  parser.parse_args()

	convert_bertabs_checkpoints(
	    args.bertabs_checkpoint_path,
	    args.pytorch_dump_folder_path,
	)









"""simple docstring"""





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						complex						,							_lowerCamelCase			:						str = "x"						,							_lowerCamelCase			:						float = 10**-10						,							_lowerCamelCase			:						int = 1						,							) ->							complex:
  lowerCamelCase_       	=		symbols(_lowerCamelCase					)
  lowerCamelCase_       	=		lambdify(_lowerCamelCase						,							_lowerCamelCase					)
  lowerCamelCase_       	=		lambdify(_lowerCamelCase						,							diff(_lowerCamelCase						,							_lowerCamelCase					)					)

  lowerCamelCase_       	=		starting_point

  while True:
    if diff_function(_lowerCamelCase					) != 0:
      lowerCamelCase_       	=		prev_guess - multiplicity * func(_lowerCamelCase					) / diff_function(
          _lowerCamelCase					)
    else:
      raise ZeroDivisionError('Could not find root'					) from None

    # Precision is checked by comparing the difference of consecutive guesses
    if abs(next_guess - prev_guess					) < precision:
      return next_guess

    lowerCamelCase_       	=		next_guess


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(F'''The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}''')

  # Find root of polynomial
  # Find fourth Root of 5
  print(F'''The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}''')

  # Find value of e
  print(
      '''The root of log(y) - 1 = 0 is ''',
      F'''{newton_raphson('log(y) - 1', 2, variable='y')}''',
  )

  # Exponential Roots
  print(
      '''The root of exp(x) - 1 = 0 is''',
      F'''{newton_raphson('exp(x) - 1', 10, precision=0.005)}''',
  )

  # Find root of cos(x)
  print(F'''The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}''')




